Reverse engineering highlights potential principles of large gene regulatory network design and learning

Gene Regulatory Networks: design and learning principles This work by Carré et al addresses central questions in biology, which are: how very large gene regulatory networks (GRNs) are organized, generate stable gene expression, and can be learnt using machine learning algorithms? In this work author...

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Autores principales:	Clément Carré, André Mas, Gabriel Krouk
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/b2ff80f10af5488bad23cb20ccd83369
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spelling	oai:doaj.org-article:b2ff80f10af5488bad23cb20ccd833692021-12-02T11:41:50ZReverse engineering highlights potential principles of large gene regulatory network design and learning10.1038/s41540-017-0019-y2056-7189https://doaj.org/article/b2ff80f10af5488bad23cb20ccd833692017-06-01T00:00:00Zhttps://doi.org/10.1038/s41540-017-0019-yhttps://doaj.org/toc/2056-7189Gene Regulatory Networks: design and learning principles This work by Carré et al addresses central questions in biology, which are: how very large gene regulatory networks (GRNs) are organized, generate stable gene expression, and can be learnt using machine learning algorithms? In this work authors developed an algorithm able to simulate large GRNs. From these networks they simulate stable or oscillating gene expression and highlights some mathematical rules controlling such a collective (several thousands of genes) behavior. They discuss consequent hypothesis concerning the organization of GRNs in real cells. Using this simulation tool, authors also demonstrate that it’s likely possible to computationally learn GRNs from transcriptomic data and prior knowledge on the network (actual known connections issued from Yeast One Hybrid or ChIP Seq for instance). They particularly highlight the crucial importance of the prior knowledge structure in their capacity to learn large GRNs.Clément CarréAndré MasGabriel KroukNature PortfolioarticleBiology (General)QH301-705.5ENnpj Systems Biology and Applications, Vol 3, Iss 1, Pp 1-15 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Clément Carré André Mas Gabriel Krouk Reverse engineering highlights potential principles of large gene regulatory network design and learning
description	Gene Regulatory Networks: design and learning principles This work by Carré et al addresses central questions in biology, which are: how very large gene regulatory networks (GRNs) are organized, generate stable gene expression, and can be learnt using machine learning algorithms? In this work authors developed an algorithm able to simulate large GRNs. From these networks they simulate stable or oscillating gene expression and highlights some mathematical rules controlling such a collective (several thousands of genes) behavior. They discuss consequent hypothesis concerning the organization of GRNs in real cells. Using this simulation tool, authors also demonstrate that it’s likely possible to computationally learn GRNs from transcriptomic data and prior knowledge on the network (actual known connections issued from Yeast One Hybrid or ChIP Seq for instance). They particularly highlight the crucial importance of the prior knowledge structure in their capacity to learn large GRNs.
format	article
author	Clément Carré André Mas Gabriel Krouk
author_facet	Clément Carré André Mas Gabriel Krouk
author_sort	Clément Carré
title	Reverse engineering highlights potential principles of large gene regulatory network design and learning
title_short	Reverse engineering highlights potential principles of large gene regulatory network design and learning
title_full	Reverse engineering highlights potential principles of large gene regulatory network design and learning
title_fullStr	Reverse engineering highlights potential principles of large gene regulatory network design and learning
title_full_unstemmed	Reverse engineering highlights potential principles of large gene regulatory network design and learning
title_sort	reverse engineering highlights potential principles of large gene regulatory network design and learning
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/b2ff80f10af5488bad23cb20ccd83369
work_keys_str_mv	AT clementcarre reverseengineeringhighlightspotentialprinciplesoflargegeneregulatorynetworkdesignandlearning AT andremas reverseengineeringhighlightspotentialprinciplesoflargegeneregulatorynetworkdesignandlearning AT gabrielkrouk reverseengineeringhighlightspotentialprinciplesoflargegeneregulatorynetworkdesignandlearning
_version_	1718395420934144000

Reverse engineering highlights potential principles of large gene regulatory network design and learning

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